Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps. We propose a model-free optimization (MFO) method based on a score gradient estimation algorithm for computationally efficient in situ training of optical computing systems. This approach treats an optical computing system as a black box and back-propagates the loss directly to the optical computing weights' probability distributions, circumventing the need for a computationally heavy and biased system simulation. Our experiments on a single-layer diffractive optical computing system show that MFO outperforms hybrid training on the MNIST and FMNIST datasets. Furthermore, we demonstrate image-free and high-speed classification of cells from their phase maps. Our method's model-free and high-performance nature, combined with its low demand for computational resources, expedites the transition of optical computing from laboratory demonstrations to real-world applications.

翻译：光学计算系统具备高速低能耗数据处理能力，但面临计算密集型的训练需求以及仿真与现实的差距等缺陷。我们提出一种基于分数梯度估计算法的无模型优化方法，用于光学计算系统的高效原位训练。该方法将光学计算系统视为黑箱，直接将损失反向传播至光学计算权重的概率分布，从而避免了对计算量大且存在偏差的系统仿真的需求。在单层衍射光学计算系统上的实验表明，MFO在MNIST和FMNIST数据集上的表现优于混合训练方法。此外，我们展示了基于细胞相位图的无图像高速细胞分类。该方法兼具无模型特性与高性能表现，且对计算资源需求低，将推动光学计算从实验室演示走向实际应用。